To train a dog using Pavlovian conditioning, we provide a stimulus and reward a response over and over again. This is supervised machine learning in a nutshell. By contrast, to teach a human, we would just say, "This is a dinner bell. When I ring it, I will give you food.” We would need at most a couple repetitions to teach this behavior. And how was this done? We didn’t provide labeled data; we put the "dinner bell” idea into your head. Can we do something similar to teach a machine?
Ben Vigoda introduces a new approach to machine learning called idea learning—teaching with ideas instead of labeled data—and demonstrates use cases with state-of-the-art performance in data applications involving structuring of product information, customer feedback, and AI/digital assistant requests.
Benjamin Vigoda is the CEO of Gamalon. Previously, Ben was technical cofounder and CEO of Lyric Semiconductor, a startup that created the first integrated circuits and processor architectures for statistical machine learning and signal processing, and a cofounder of Design That Matters, a not-for-profit that, for the past decade, has helped solve engineering and design problems in underserved communities and has saved thousands of infant lives by developing low-cost, easy-to-use medical technology such as infant incubators, UV therapy, pulse oximeters, and IV drip systems that have been fielded in 20 countries. Lyric Semiconductor was named one of the “50 most innovative companies” by Technology Review and was featured in the Wall Street Journal, New York Times, EE Times, Scientific American, Wired, and other media. Lyric was successfully acquired by Analog Devices, and Lyric’s products and technology are being deployed in leading smartphones and consumer electronics, medical devices, wireless base stations, and automobiles. Ben has won entrepreneurship competitions at MIT and Harvard and fellowships from Intel and the Kavli Foundation and National Academy of Sciences and has held research appointments at MIT, HP, Mitsubishi, and the Santa Fe Institute. Ben has authored over 120 patents and academic publications. He’s serving on the DARPA Information Science and Technology (ISAT) steering committee. Ben holds a PhD from MIT, where he developed circuits for implementing machine learning algorithms natively in hardware.
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